Sync widgets demo

packages/inference/src/tasks/nlp/textGeneration.ts

@@ -1,8 +1,210 @@
-import type { TextGenerationInput, TextGenerationOutput } from "@huggingface/tasks/src/tasks/text-generation/inference";
 import { InferenceOutputError } from "../../lib/InferenceOutputError";
 import type { BaseArgs, Options } from "../../types";
 import { request } from "../custom/request";
 
+/**
+ * Inputs for Text Generation inference
+ */
+export interface TextGenerationInput {
+	/**
+	 * The text to initialize generation with
+	 */
+	inputs: string;
+	/**
+	 * Additional inference parameters
+	 */
+	parameters?: TextGenerationParameters;
+	/**
+	 * Whether to stream output tokens
+	 */
+	stream?: boolean;
+	[property: string]: unknown;
+}
+
+/**
+ * Additional inference parameters
+ *
+ * Additional inference parameters for Text Generation
+ */
+export interface TextGenerationParameters {
+	/**
+	 * The number of sampling queries to run. Only the best one (in terms of total logprob) will
+	 * be returned.
+	 */
+	best_of?: number;
+	/**
+	 * Whether or not to output decoder input details
+	 */
+	decoder_input_details?: boolean;
+	/**
+	 * Whether or not to output details
+	 */
+	details?: boolean;
+	/**
+	 * Whether to use logits sampling instead of greedy decoding when generating new tokens.
+	 */
+	do_sample?: boolean;
+	/**
+	 * The maximum number of tokens to generate.
+	 */
+	max_new_tokens?: number;
+	/**
+	 * The parameter for repetition penalty. A value of 1.0 means no penalty. See [this
+	 * paper](https://hf.co/papers/1909.05858) for more details.
+	 */
+	repetition_penalty?: number;
+	/**
+	 * Whether to prepend the prompt to the generated text.
+	 */
+	return_full_text?: boolean;
+	/**
+	 * The random sampling seed.
+	 */
+	seed?: number;
+	/**
+	 * Stop generating tokens if a member of `stop_sequences` is generated.
+	 */
+	stop_sequences?: string[];
+	/**
+	 * The value used to modulate the logits distribution.
+	 */
+	temperature?: number;
+	/**
+	 * The number of highest probability vocabulary tokens to keep for top-k-filtering.
+	 */
+	top_k?: number;
+	/**
+	 * If set to < 1, only the smallest set of most probable tokens with probabilities that add
+	 * up to `top_p` or higher are kept for generation.
+	 */
+	top_p?: number;
+	/**
+	 * Truncate input tokens to the given size.
+	 */
+	truncate?: number;
+	/**
+	 * Typical Decoding mass. See [Typical Decoding for Natural Language
+	 * Generation](https://hf.co/papers/2202.00666) for more information
+	 */
+	typical_p?: number;
+	/**
+	 * Watermarking with [A Watermark for Large Language Models](https://hf.co/papers/2301.10226)
+	 */
+	watermark?: boolean;
+	[property: string]: unknown;
+}
+
+/**
+ * Outputs for Text Generation inference
+ */
+export interface TextGenerationOutput {
+	/**
+	 * When enabled, details about the generation
+	 */
+	details?: TextGenerationOutputDetails;
+	/**
+	 * The generated text
+	 */
+	generated_text: string;
+	[property: string]: unknown;
+}
+
+/**
+ * When enabled, details about the generation
+ */
+export interface TextGenerationOutputDetails {
+	/**
+	 * Details about additional sequences when best_of is provided
+	 */
+	best_of_sequences?: TextGenerationOutputSequenceDetails[];
+	/**
+	 * The reason why the generation was stopped.
+	 */
+	finish_reason: TextGenerationFinishReason;
+	/**
+	 * The number of generated tokens
+	 */
+	generated_tokens: number;
+	prefill: TextGenerationPrefillToken[];
+	/**
+	 * The random seed used for generation
+	 */
+	seed?: number;
+	/**
+	 * The generated tokens and associated details
+	 */
+	tokens: TextGenerationOutputToken[];
+	/**
+	 * Most likely tokens
+	 */
+	top_tokens?: Array<TextGenerationOutputToken[]>;
+	[property: string]: unknown;
+}
+
+export interface TextGenerationOutputSequenceDetails {
+	finish_reason: TextGenerationFinishReason;
+	/**
+	 * The generated text
+	 */
+	generated_text: string;
+	/**
+	 * The number of generated tokens
+	 */
+	generated_tokens: number;
+	prefill: TextGenerationPrefillToken[];
+	/**
+	 * The random seed used for generation
+	 */
+	seed?: number;
+	/**
+	 * The generated tokens and associated details
+	 */
+	tokens: TextGenerationOutputToken[];
+	/**
+	 * Most likely tokens
+	 */
+	top_tokens?: Array<TextGenerationOutputToken[]>;
+	[property: string]: unknown;
+}
+
+export interface TextGenerationPrefillToken {
+	id: number;
+	logprob: number;
+	/**
+	 * The text associated with that token
+	 */
+	text: string;
+	[property: string]: unknown;
+}
+
+/**
+ * Generated token.
+ */
+export interface TextGenerationOutputToken {
+	id: number;
+	logprob?: number;
+	/**
+	 * Whether or not that token is a special one
+	 */
+	special: boolean;
+	/**
+	 * The text associated with that token
+	 */
+	text: string;
+	[property: string]: unknown;
+}
+
+/**
+ * The reason why the generation was stopped.
+ *
+ * length: The generated sequence reached the maximum allowed length
+ *
+ * eos_token: The model generated an end-of-sentence (EOS) token
+ *
+ * stop_sequence: One of the sequence in stop_sequences was generated
+ */
+export type TextGenerationFinishReason = "length" | "eos_token" | "stop_sequence";
+
 /**
  * Use to continue text from a prompt. This is a very generic task. Recommended model: gpt2 (it’s a simple model, but fun to play with).
  */

packages/inference/src/tasks/nlp/textGenerationStream.ts

@@ -1,7 +1,6 @@
 import type { BaseArgs, Options } from "../../types";
 import { streamingRequest } from "../custom/streamingRequest";
-
-import type { TextGenerationInput } from "@huggingface/tasks/src/tasks/text-generation/inference";
+import type { TextGenerationInput } from "./textGeneration";
 
 export interface TextGenerationStreamToken {
 	/** Token ID from the model tokenizer */

packages/widgets/src/lib/components/InferenceWidget/shared/WidgetExamples/WidgetExamples.svelte

@@ -49,6 +49,9 @@
 	}
 
 	function _applyWidgetExample(idx: number) {
+		if (!isOptionsVisible) {
+			return;
+		}
 		hideOptions();
 		const sample = examples[idx];
 		title = sample.example_title as string;
@@ -56,6 +59,9 @@
 	}
 
 	function _previewInputSample(idx: number) {
+		if (!isOptionsVisible) {
+			return;
+		}
 		const sample = examples[idx];
 		applyWidgetExample(sample, { isPreview: true });
 	}

packages/widgets/src/lib/components/InferenceWidget/widgets/ConversationalWidget/ConversationalWidget.svelte

@@ -214,24 +214,16 @@
 	}
 
 	async function applyWidgetExample(example: Example, opts: ExampleRunOpts = {}): Promise<void> {
-		if (isLoading) {
-			return;
+		if ("text" in example) {
+			messages = [{ role: "user", content: example.text }];
+		} else {
+			messages = [...example.messages];
 		}
-		isLoading = true;
-		try {
-			if ("text" in example) {
-				messages = [{ role: "user", content: example.text }];
-			} else {
-				messages = [...example.messages];
-			}
-			if (opts.isPreview) {
-				return;
-			}
-			const exampleOutput = example.output;
-			await getOutput({ ...opts.inferenceOpts, exampleOutput });
-		} finally {
-			isLoading = false;
+		if (opts.isPreview) {
+			return;
 		}
+		const exampleOutput = example.output;
+		await getOutput({ ...opts.inferenceOpts, exampleOutput });
 	}
 
 	function validateExample(sample: WidgetExample): sample is Example {